Forwarded from Code With Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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Selection for those who want to become a certified Claude architect
Useful resources for preparation in one place 👇
• Registration for certification: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request
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• Cookbook (examples and practices):
https://github.com/anthropics/anthropic-cookbook
• Exam guide:
https://share.google/0eqIbebzRMUt8KTc8
• Practice questions:
https://claudecertifications.com
• MCP documentation:
https://modelcontextprotocol.io
API documentation:
https://docs.anthropic.com
Useful playbook:
https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view
Useful resources for preparation in one place 👇
• Registration for certification: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request
• Training (13 free courses):
https://anthropic.skilljar.com
• Cookbook (examples and practices):
https://github.com/anthropics/anthropic-cookbook
• Exam guide:
https://share.google/0eqIbebzRMUt8KTc8
• Practice questions:
https://claudecertifications.com
• MCP documentation:
https://modelcontextprotocol.io
API documentation:
https://docs.anthropic.com
Useful playbook:
https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view
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Build a Large Language Model from Scratch! 🚀
This repository provides code examples for developing, pretraining, and fine-tuning a Large Language Model (LLM) from the ground up. It serves as the official codebase for the book "Build a Large Language Model (From Scratch)." 📘
Notebook examples are included for each chapter:
Chapter 1: Understanding Large Language Models 🧠
Chapter 2: Working with Text Data 📝
Chapter 3: Coding Attention Mechanisms ⚙️
Chapter 4: Implementing a GPT Model from Scratch 🏗
Chapter 5: Pretraining on Unlabeled Data 📊
Chapter 6: Fine-tuning for Text Classification 🏷
Chapter 7: Fine-tuning to Follow Instructions 🗣
Repository: https://github.com/rasbt/LLMs-from-scratch 🔗
This repository provides code examples for developing, pretraining, and fine-tuning a Large Language Model (LLM) from the ground up. It serves as the official codebase for the book "Build a Large Language Model (From Scratch)." 📘
Notebook examples are included for each chapter:
Chapter 1: Understanding Large Language Models 🧠
Chapter 2: Working with Text Data 📝
Chapter 3: Coding Attention Mechanisms ⚙️
Chapter 4: Implementing a GPT Model from Scratch 🏗
Chapter 5: Pretraining on Unlabeled Data 📊
Chapter 6: Fine-tuning for Text Classification 🏷
Chapter 7: Fine-tuning to Follow Instructions 🗣
Repository: https://github.com/rasbt/LLMs-from-scratch 🔗
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Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🚀 Fine-Tuning Large Language Models for Domain-Specific Tasks
Fine-tuning Large Language Models is the process by which generic LLMs are transformed into domain-specific experts. This procedure updates model weights using task-specific labeled data, rather than relying solely on prompting or retrieval mechanisms. This approach is particularly effective when language patterns remain stable and consistent outputs are required.
👉 Core Concept
A pre-trained LLM acquires general language capabilities. Fine-tuning instructs the model on how language functions within specific domains, such as healthcare, finance, legal services, or internal enterprise workflows.
👉 Practical Implementation
A customer support model is trained on thousands of instruction-response pairs. For example:
Input: Refund request for a delayed shipment
Output: A policy-compliant response including an apology, procedural steps, and a resolution.
Following fine-tuning, the model generates consistent, policy-aligned answers with lower latency compared to Retrieval-Augmented Generation (RAG).
👉 Significance of Parameter-Efficient Fine-Tuning
Techniques such as LoRA and QLoRA train only small adapter layers while keeping the base model frozen. This methodology reduces GPU memory consumption, accelerates training, and enables the fine-tuning of large models on hardware with limited resources.
👉 Appropriate Use Cases for Fine-Tuning
- Recurring domain-specific language
- Structured outputs, including classifications, summaries, or templates
- Stable knowledge bases that do not undergo daily changes
- Latency-sensitive systems where retrieval introduces overhead
Typical Production Stack
- Models: LLaMA or Mistral
- Frameworks: PyTorch with Hugging Face and PEFT
- Optimization: DeepSpeed or Accelerate
- Deployment: FastAPI, Docker, and cloud GPUs
💡 Fine-tuning enhances accuracy, consistency, and cost efficiency when applied to suitable problems.
Fine-tuning Large Language Models is the process by which generic LLMs are transformed into domain-specific experts. This procedure updates model weights using task-specific labeled data, rather than relying solely on prompting or retrieval mechanisms. This approach is particularly effective when language patterns remain stable and consistent outputs are required.
👉 Core Concept
A pre-trained LLM acquires general language capabilities. Fine-tuning instructs the model on how language functions within specific domains, such as healthcare, finance, legal services, or internal enterprise workflows.
👉 Practical Implementation
A customer support model is trained on thousands of instruction-response pairs. For example:
Input: Refund request for a delayed shipment
Output: A policy-compliant response including an apology, procedural steps, and a resolution.
Following fine-tuning, the model generates consistent, policy-aligned answers with lower latency compared to Retrieval-Augmented Generation (RAG).
👉 Significance of Parameter-Efficient Fine-Tuning
Techniques such as LoRA and QLoRA train only small adapter layers while keeping the base model frozen. This methodology reduces GPU memory consumption, accelerates training, and enables the fine-tuning of large models on hardware with limited resources.
👉 Appropriate Use Cases for Fine-Tuning
- Recurring domain-specific language
- Structured outputs, including classifications, summaries, or templates
- Stable knowledge bases that do not undergo daily changes
- Latency-sensitive systems where retrieval introduces overhead
Typical Production Stack
- Models: LLaMA or Mistral
- Frameworks: PyTorch with Hugging Face and PEFT
- Optimization: DeepSpeed or Accelerate
- Deployment: FastAPI, Docker, and cloud GPUs
💡 Fine-tuning enhances accuracy, consistency, and cost efficiency when applied to suitable problems.
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Forwarded from Udemy Free Coupons
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Python Complete Course For Python Beginners.Learn Python From Beginner To Advanced Level...
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A new open-source Python library titled "Fli" has been released, offering direct access to Google Flights. This library circumvents the web interface by interfacing directly with a reverse-engineered API to deliver rapid and structured results. The project is 100% open-source.
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The 10 Most Valuable AI Learning Repositories on GitHub 👇
I pulled the top 10 repos where Jupyter is the main language
Filtered for the best educational resources
Here's what's worth your time :
1. microsoft/generative-ai-for-beginners ⭐ 105,577 21
lessons covering the full GenAI stack From prompting basics to production deployment Built by Microsoft's AI education team
🔗 https://lnkd.in/diW9Cca6
2. rasbt/LLMs-from-scratch ⭐ 83,714
Build GPT-like models from zero No hand-waving, pure implementation Companion to Sebastian Raschka's book
🔗 https://lnkd.in/d3cq5diH
3. microsoft/ai-agents-for-beginners ⭐ 49,333
Complete course on agentic systems Covers planning, tools, memory, multi-agent Released 3 months ago, already essential
🔗 https://lnkd.in/e-a2gqSv
4. microsoft/ML-For-Beginners ⭐ 83,279
12 weeks of classical ML fundamentals 26 lessons, 52 quizzes, full curriculum Still relevant despite the LLM hype
🔗 https://lnkd.in/e7S8yDbS
5. openai/openai-cookbook ⭐ 71,106
Official OpenAI examples and guides Real production patterns, not toys Updated constantly with new features
🔗 https://lnkd.in/dtMbuMGk
6. jackfrued/Python-100-Days ⭐ 177,958
Most-starred educational repo on GitHub 100 days from Python beginner to advanced Covers web dev, data science, automation
🔗 https://lnkd.in/duWVtn4i
7. pathwaycom/llm-app ⭐ 54,583
Production RAG templates you can deploy Real-time data pipelines, not static demos Enterprise search with live updates
🔗 https://lnkd.in/daUFK9Nd
8. jakevdp/PythonDataScienceHandbook ⭐ 46,574
Entire data science handbook as Jupyter notebooks NumPy, Pandas, Matplotlib, Scikit-Learn Free alternative to $60 textbook
🔗 https://lnkd.in/db8HP7vT
9. CompVis/stable-diffusion ⭐ 72,246
Original Stable Diffusion implementation Understand how text-to-image actually works Foundation for SDXL, Midjourney competitors
🔗 https://lnkd.in/dEya2Rb5
10. facebookresearch/segment-anything ⭐ 53,250
Meta's SAM model for computer vision Promptable segmentation in images and videos Powers modern AI video editing tools
🔗 https://lnkd.in/dKvjk6Yb
I pulled the top 10 repos where Jupyter is the main language
Filtered for the best educational resources
Here's what's worth your time :
1. microsoft/generative-ai-for-beginners ⭐ 105,577 21
lessons covering the full GenAI stack From prompting basics to production deployment Built by Microsoft's AI education team
🔗 https://lnkd.in/diW9Cca6
2. rasbt/LLMs-from-scratch ⭐ 83,714
Build GPT-like models from zero No hand-waving, pure implementation Companion to Sebastian Raschka's book
🔗 https://lnkd.in/d3cq5diH
3. microsoft/ai-agents-for-beginners ⭐ 49,333
Complete course on agentic systems Covers planning, tools, memory, multi-agent Released 3 months ago, already essential
🔗 https://lnkd.in/e-a2gqSv
4. microsoft/ML-For-Beginners ⭐ 83,279
12 weeks of classical ML fundamentals 26 lessons, 52 quizzes, full curriculum Still relevant despite the LLM hype
🔗 https://lnkd.in/e7S8yDbS
5. openai/openai-cookbook ⭐ 71,106
Official OpenAI examples and guides Real production patterns, not toys Updated constantly with new features
🔗 https://lnkd.in/dtMbuMGk
6. jackfrued/Python-100-Days ⭐ 177,958
Most-starred educational repo on GitHub 100 days from Python beginner to advanced Covers web dev, data science, automation
🔗 https://lnkd.in/duWVtn4i
7. pathwaycom/llm-app ⭐ 54,583
Production RAG templates you can deploy Real-time data pipelines, not static demos Enterprise search with live updates
🔗 https://lnkd.in/daUFK9Nd
8. jakevdp/PythonDataScienceHandbook ⭐ 46,574
Entire data science handbook as Jupyter notebooks NumPy, Pandas, Matplotlib, Scikit-Learn Free alternative to $60 textbook
🔗 https://lnkd.in/db8HP7vT
9. CompVis/stable-diffusion ⭐ 72,246
Original Stable Diffusion implementation Understand how text-to-image actually works Foundation for SDXL, Midjourney competitors
🔗 https://lnkd.in/dEya2Rb5
10. facebookresearch/segment-anything ⭐ 53,250
Meta's SAM model for computer vision Promptable segmentation in images and videos Powers modern AI video editing tools
🔗 https://lnkd.in/dKvjk6Yb
❤5